Perception and abstraction

A child might learn what growing means by observing a sibling, a pet or a plant get physically bigger, but once understood, the same idea of growing can be applied to pocket money, a tummy ache or Dad’s age. This ability to represent relations, principles or ideas like ‘growing’ with sufficient abstraction that they can be flexibly (re-)applied in disparate, and potentially unfamiliar, contexts and domains is central to human cognition and language. Our work studies how this ability can be replicated in distributed learning systems like neural networks.